A radically different approach was proposed in [1]}: Given a collection of 3D surface meshes and corresponding results of simulations, it is possible to train a Geodesic Convolutional Neural Networks (GCNNs) [2]}, [3]} to emulate a complex fluid-dynamics simulator. This makes it possible to write a differentiable objective function that returns aerodynamic and hydrodynamic properties—drag, lift, stability—very fast and therefore to maximize performance with respect to the 3D shape of the target objects, no matter how many parameters are required to define its shape. That gives it an edge over Kriging that can only deal with relatively fewer parameters. However, unlike Kriging, it suffers from the fact that it does not account for the potential imprecision of its prediction.
